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<p>The rapidly growing revolution of generative Artificial Intelligence software has moved into the counseling and disseminating synthetic images, thereby establishing a new paradigm for machine learning models. This study investigates the impact of combining real-world and AI-generated synthetic images on the performance of image classification models. Using three traffic-related datasets—potholes, speed bumps, and traffic lights—we applied data augmentation and tested seven configurations with varying real-to-synthetic image ratios. The DenseNet201 model, fine-tuned with the Adam optimizer, was used for all experiments. Results show that a 1:3 real-to-synthetic ratio enhances classification accuracy and generalization, with the highest validation accuracy reaching 97.36%. Our findings demonstrate that synthetic data, when properly integrated, serves as a cost-effective and scalable complement to real data, especially in scenarios with limited labeled samples.</p>

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Bridging Reality and Synthetics: Optimizing Image Classification with Hybrid AI-Generated and Real-World Datasets

  • 期刊论文

  • 发表日期：

    2025年7月11日

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  • Abdallah Tariq Hasan Alabed,
  • Jawad Rasheed,
  • Mirsat Yesiltepe,
  • Shtwai Alsubai,
  • Tunc Asuroglu

摘要

The rapidly growing revolution of generative Artificial Intelligence software has moved into the counseling and disseminating synthetic images, thereby establishing a new paradigm for machine learning models. This study investigates the impact of combining real-world and AI-generated synthetic images on the performance of image classification models. Using three traffic-related datasets—potholes, speed bumps, and traffic lights—we applied data augmentation and tested seven configurations with varying real-to-synthetic image ratios. The DenseNet201 model, fine-tuned with the Adam optimizer, was used for all experiments. Results show that a 1:3 real-to-synthetic ratio enhances classification accuracy and generalization, with the highest validation accuracy reaching 97.36%. Our findings demonstrate that synthetic data, when properly integrated, serves as a cost-effective and scalable complement to real data, especially in scenarios with limited labeled samples.